8 min read
This study guide covers the Central Limit Theorem (CLT), focusing on its definition, conditions (sample size, independence, random sample or SIR), importance for inference and probability calculations, and application to sample means. It explains the impact of sample size on sampling distributions, provides example scenarios, visualizations, and emphasizes common mistakes to avoid. The guide includes practice multiple-choice and free-response questions with solutions and scoring breakdowns to aid exam preparation.
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Question 1 of 10
When we take many random samples from any population, what is the approximate distribution of the sample means? ๐ค
The same as the population's distribution
Approximately normal
Always uniform
Always skewed